Predictive entropy.
As a way to obtain additional insight on how informative each region feature is for the bounding box categorization task, we form the representative attentive class prototype as the sum of the attentive region features \(f^{c_j}_{att}\)  weighted with the predictive entropy [1]}, [2]} of the classifier \(C_{\phi _s}\) , denoted by \(\mathcal {H}(Y|x;\Theta )\) . Instead of utilizing the uncertainty of the model as in [2]}, we weigh each attentive region feature with the confidence of the associated prediction. The predictive entropy and the resulting entropy weights are given by
\(\nonumber \mathcal {H}(Y|x;\Theta ) = -\sum _{c=1}^{N_c}p_\Theta (Y=c|x)\log \lbrace p_\Theta (Y=c|x)\rbrace ,\) 
\(E = 1 - \frac{\mathcal {H}(Y|x;\Theta )}{\sum _{x\in X}\mathcal {H}(Y|x;\Theta )}.\) 
